Run two Claude Code accounts simultaneously on macOS without re-authenticating by using separate configuration directories.
- Create Separate Config Directories
mkdir ~/.claude-account1 mkdir ~/.claude-account2
| [NativeHelper::Init] Parsed 7132 natives in 86 namespaces | |
| Original Hash: 0x14D29BB12D47F68C, Current Hash: 0x14D29BB12D47F68C, Address: 0x11CC10 SET_CONTROL_TRIGGER_SHAKE | |
| Original Hash: 0xC964FCD3D1720697, Current Hash: 0xC964FCD3D1720697, Address: 0x11CC10 _0xC964FCD3D1720697 | |
| Original Hash: 0x0CC28C08613BA9E5, Current Hash: 0x0CC28C08613BA9E5, Address: 0x11CC10 _0x0CC28C08613BA9E5 | |
| Original Hash: 0x19ABCC581D28E6F9, Current Hash: 0x19ABCC581D28E6F9, Address: 0x11CC10 SET_ALL_MAPDATA_CULLED | |
| Original Hash: 0x72751156E7678833, Current Hash: 0x72751156E7678833, Address: 0x11CC10 SET_IGNORE_NO_GPS_FLAG | |
| Original Hash: 0xA9342743B634A462, Current Hash: 0xA9342743B634A462, Address: 0x11CC10 _0xA9342743B634A462 | |
| Original Hash: 0x94B261F1F35293E1, Current Hash: 0x94B261F1F35293E1, Address: 0x11CC10 _0x94B261F1F35293E1 | |
| Original Hash: 0x160F0CE6D76A39C9, Current Hash: 0x160F0CE6D76A39C9, Address: 0x11CC10 _0x160F0CE6D76A39C9 | |
| Original Hash: 0xFC6FCF4C03F1BBF6, Current Hash: 0xFC6FCF4C03F1BBF6, Address: 0x11CC10 _0xFC6FCF4C |
cd /d %ProgramFiles(x86)%\Microsoft Office\Office16
cd /d %ProgramFiles%\Microsoft Office\Office16
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
Most Flutter routers were architected before Dart 3 had sealed types and pattern matching. One has been redesigned around them. Here's the value-oriented shape no one has built yet.
Flutter's two most-adopted routing libraries — go_router and auto_route — were architected in a world before Dart had sealed classes, exhaustive pattern matching, and records. Both shipped their core API in 2021–2022, and both still organize themselves around the same primitive: a string path, parsed at runtime, with type safety bolted on via build_runner.
| # EDIT - 01 Dec 2023 - Works without the below in Ubuntnu 22.04 | |
| $ lsusb | |
| Bus 002 Device 001: ID 1d6b:0003 Linux Foundation 3.0 root hub | |
| Bus 001 Device 040: ID 0a5c:21e8 Broadcom Corp. BCM20702A0 Bluetooth 4.0 | |
| Bus 001 Device 002: ID 05e3:0608 Genesys Logic, Inc. Hub | |
| Bus 001 Device 044: ID 0a12:0001 Cambridge Silicon Radio, Ltd Bluetooth Dongle (HCI mode) | |
| Bus 001 Device 001: ID 1d6b:0002 Linux Foundation 2.0 root hub | |
| # get the pre-compiled driver |
| README.md | |
| manifest.json | |
| background.js | |
| content.js | |
| popup.html | |
| popup.js | |
| popup.css | |
| options.html | |
| options.js | |
| auth.js |
Resumidamente, o Gitflow é um modelo fortemente baseado em branches, mas focado nas entregas. Foi criado em 2010 e hoje em dia é muito utilizado por equipes de desenvolvedores em todo o mundo.
Ao invés de trabalhar apenas com a branch master, esse workflow utiliza dois branches principais para guardar histórico do projeto. A branch master guarda o histórico oficial das entregas, já a branch developer serve como integração entre todas as branches de funcionalidades (feature branches).